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An emerging trend in music information retrieval (MIR) is the use of supervised machine learning to train automatic music transcription models. A prerequisite of adopting a machine learning methodology is the availability of annotated corpora. However, different genres of music have different characteristics and modelling these characteristics is an important part of creating state of the art MIR systems. Consequently, although some music corpora are available the use of these corpora is tied to the specific music genre, instrument type and recording context the corpus covers. This paper introduces the first corpus of annotations of audio recordings of Irish traditional dance music that covers multiple instrument types and both solo studio and live session recordings. We first discuss the considerations that motivated our design choices in developing the corpus. We then benchmark a number of automatic music transcription algorithms against the corpus.
Beauguitte, P., Duggan, B., & Kelleher, J. (2016). A Corpus of Annotated Irish Traditional Dance Music Recordings: Design and Benchmark Evaluations. 17th International Society for Music Information Retrieval Conference 2016, New York City, August 7-11. doi:10.5072/zenodo.242990